96 research outputs found
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Visual robot navigation within large-scale, semi-structured environments
deals with various challenges such as computation intensive path planning
algorithms or insufficient knowledge about traversable spaces. Moreover, many
state-of-the-art navigation approaches only operate locally instead of gaining
a more conceptual understanding of the planning objective. This limits the
complexity of tasks a robot can accomplish and makes it harder to deal with
uncertainties that are present in the context of real-time robotics
applications. In this work, we present Topomap, a framework which simplifies
the navigation task by providing a map to the robot which is tailored for path
planning use. This novel approach transforms a sparse feature-based map from a
visual Simultaneous Localization And Mapping (SLAM) system into a
three-dimensional topological map. This is done in two steps. First, we extract
occupancy information directly from the noisy sparse point cloud. Then, we
create a set of convex free-space clusters, which are the vertices of the
topological map. We show that this representation improves the efficiency of
global planning, and we provide a complete derivation of our algorithm.
Planning experiments on real world datasets demonstrate that we achieve similar
performance as RRT* with significantly lower computation times and storage
requirements. Finally, we test our algorithm on a mobile robotic platform to
prove its advantages.Comment: 8 page
Learning Topometric Semantic Maps from Occupancy Grids
Today's mobile robots are expected to operate in complex environments they
share with humans. To allow intuitive human-robot collaboration, robots require
a human-like understanding of their surroundings in terms of semantically
classified instances. In this paper, we propose a new approach for deriving
such instance-based semantic maps purely from occupancy grids. We employ a
combination of deep learning techniques to detect, segment and extract door
hypotheses from a random-sized map. The extraction is followed by a
post-processing chain to further increase the accuracy of our approach, as well
as place categorization for the three classes room, door and corridor. All
detected and classified entities are described as instances specified in a
common coordinate system, while a topological map is derived to capture their
spatial links. To train our two neural networks used for detection and map
segmentation, we contribute a simulator that automatically creates and
annotates the required training data. We further provide insight into which
features are learned to detect doorways, and how the simulated training data
can be augmented to train networks for the direct application on real-world
grid maps. We evaluate our approach on several publicly available real-world
data sets. Even though the used networks are solely trained on simulated data,
our approach demonstrates high robustness and effectiveness in various
real-world indoor environments.Comment: Presented at the 2019 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS
Using and evaluating the real-time spatial perception system hydra in real-world scenarios
Hydra is a real-time machine perception system released open source in 2022 as a package for
Robot Operating System (ROS). Machine perception systems like Hydra may play a role in
the engineering of the next generation of spatial AIs for autonomous robots. Hydra is in the
preliminary stages of its existence and does not come with intrinsic support for running on
custom datasets. This thesis primarily aims to find out whether the promised capabilities of
Hydra can be replicated. As well as to establish a workflow and guidelines for what
modifications to Hydra are needed to successfully run it
Autonomous Navigation of Distributed Spacecraft using Graph-based SLAM for Proximity Operations in Small Celestial Bodies
Establishment of a sustainable human presence beyond the cislunar space is a major milestone for mankind. Small celestial bodies (SCBs) like asteroids are known to contain valuable natural resources necessary for the development of space assets essential to the accomplishment of this goal. Consequently, future robotic spacecraft missions to SCBs are envisioned with the objective of commercial in-situ resource utilization (ISRU). In mission design, there is also an increasing interest in the utilization of the distributed spacecraft, to benefit from specialization and redundancy. The ability of distributed spacecraft to navigate autonomously in the proximity of a SCB is indispensable for the successful realization of ISRU mission objectives. Quasi-autonomous methods currently used for proximity navigation require extensive ground support for mapping and model development, which can be an impediment for large scale multi-spacecraft ISRU missions in the future.
It is prudent to leverage the advances in terrestrial robotic navigation to investigate the development of novel methods for autonomous navigation of spacecraft. The primary objective of the work presented in this thesis is to evaluate the feasibility and investigate the development of methods based on graph-based simultaneous localization and mapping (SLAM), a popular algorithm used in terrestrial autonomous navigation, for the autonomous navigation of distributed spacecraft in the proximity of SCBs. To this end, recent research in graph-based SLAM is extensively studied to identify strategies used to enable multi-agent navigation. The spacecraft navigation requirement is formulated as a graph-based SLAM problem using metric GraphSLAM or topometric graph-based SLAM. Techniques developed based on the identified strategies namely, map merging, inter-spacecraft measurements and relative localization are then applied to this formulation to enable distributed spacecraft navigation. In each case, navigation is formulated in terms of its application to a proximity operation scenario that best suits the multi-agent navigation technique.
Several challenges related to the application of graph-based SLAM for spacecraft navigation, such as computational cost and illumination variation are also identified and addressed in the development of these methods. Experiments are performed using simulated models of asteroids and spacecraft dynamics, comparing the estimated states of the spacecraft and landmarks to the assumed true states. The results from the experiments indicate a consistent and robust state determination process, suggesting the suitability of the application of multi-agent navigation techniques to graph-based SLAM for enabling the autonomous navigation of distributed spacecraft near SCBs
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